update readme for clip

README.md

@@ -1,6 +1,7 @@
 ---
 task_categories:
 - text-retrieval
+- image-to-text
 - sentence-similarity
 language:
 - en
@@ -24,8 +25,12 @@ It is specifically designed for integration with VectorDBBench.
 
 ### Dataset Summary
 
-- **Source Data**: Wikipedia (Public Dataset)
-- **Embedding Model**: [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)
+- **Source Data**: 
+  - Wikipedia (Public Dataset)
+  - LAION0400M (Public Dataset)
+- **Embedding Model**: 
+  - google/embeddinggemma-300m
+  - sentence-transformers/clip-ViT-B-32
 
 ## Dataset Structure
 
@@ -34,7 +39,6 @@ For each embedding model, the directory contains two key file:
 | Data | Description |
 |-------|-------------|
 | `centroids.npy` | centroids as followed IVF |
-| `tree_info.pkl` | tree metadata with parent and leaf info |
 
 ## Data Fields
 
@@ -42,35 +46,19 @@ For each embedding model, the directory contains two key file:
 
 - **Purpose**: Finding the nearest clusters for IVF (Inverted File Index)
 - **Type**: NumPy array (`np.ndarray`)
-- **Shape**: `[32768, 768]`
-- **Description**: 768-dimensional vectors representing 32,768 cluster centroids
+- **Shape**: `[32768, 768]` or `[1024, 512]`
+- **Description**: 768-dimensional vectors representing 32,768 cluster centroids, or 512-dimensional vectors representing 1,024 cluster centroids.
 - **Normalization**: L2-normalized (unit norm)
 - **Format**: float32
 
-### Tree Metadata: `tree_info.pkl`
-
-- **Purpose**: Finding virtual clusters following hierarchical tree structure for efficient GAS search
-- **Type**: Python dictionary (pickle)
-- **Keys**:
-  - `node_parents`: Dictionary mapping each node ID to its parent node ID
-    - Format: `{node_id: parent_node_id, ...}`
-    - Contains parent-child relationships for all nodes in the tree
-  
-  - `leaf_ids`: List of leaf node IDs
-    - Format: `[leaf_id_1, leaf_id_2, ..., leaf_id_32768]`
-    - Total 32,768 leaf nodes (corresponding to 32,768 centroids)
-  
-  - `leaf_to_centroid_idx`: Mapping from leaf node IDs to centroid indices in `centroids.npy`
-    - Format: `{leaf_node_id: centroid_index, ...}`
-    - Maps each leaf node to its corresponding row index in `centroids.npy`
-    - Important: Leaf IDs in `leaf_ids` are ordered sequentially, so the i-th leaf corresponds to the i-th centroid
-
 
 ## Dataset Creation
 
 ### Source Data
 
-Source dataset is a large public dataset, Wikipedia: [mixedbread-ai/wikipedia-data-en-2023-11](https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11).
+Source dataset is a large public dataset: 
+- Wikipedia: [mixedbread-ai/wikipedia-data-en-2023-11](https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11)
+- LAION: [LAION-400M](https://laion.ai/blog/laion-400-open-dataset/).
 
 ### Preprocessing
 
@@ -78,7 +66,7 @@ Source dataset is a large public dataset, Wikipedia: [mixedbread-ai/wikipedia-da
 
    Description TBD
 
-2. Chunking: For texts exceeding 2048 tokens:
+2. Chunking (for text): For texts exceeding 2048 tokens:
 
     - Split into chunks with ~100 token overlap
     - Embedded each chunk separately
@@ -88,10 +76,16 @@ Source dataset is a large public dataset, Wikipedia: [mixedbread-ai/wikipedia-da
 
 ### Embedding Generation
 
-- Model: google/embeddinggemma-300m
-- Dimension: 768
-- Max Token Length: 2048
-- Normalization: L2-normalized
+- Text:
+  - Model: google/embeddinggemma-300m
+  - Dimension: 768
+  - Max Token Length: 2048
+  - Normalization: L2-normalized
+
+- Multi-Modal:
+  - Model: sentence-transformers/clip-ViT-B-32
+  - Dimension: 512
+  - Normalization: L2-normalized
 
 ## Usage
 
@@ -99,10 +93,9 @@ Source dataset is a large public dataset, Wikipedia: [mixedbread-ai/wikipedia-da
 import wget
 
 def download_centroids(embedding_model: str, dataset_dir: str) -> None:
-    """Download pre-computed centroids and tree info for GAS."""
-    dataset_link = "https://huggingface.co/datasets/cryptolab-playground/gas-centroids/resolve/main/embeddinggemma-300m"
+    """Download pre-computed centroids for IVF_GAS."""
+    dataset_link = f"https://huggingface.co/datasets/cryptolab-playground/gas-centroids/resolve/main/{embedding_model}"
     wget.download(f"{dataset_link}/centroids.npy", out="centroids.npy")
-    wget.download(f"{dataset_link}/tree_info.pkl", out="tree_info.pkl")
 ```
 
 ## License
@@ -135,6 +128,16 @@ If you use this dataset, please cite:
 }
 ```
 
+```bibtex
+@dataset{laion400m,
+  author = {Schuhmann, Christoph and others},
+  title = {LAION-AI},
+  year = {2021},
+  publisher = {LAION},
+  url = {https://laion.ai/blog/laion-400-open-dataset}
+}
+```
+
 ### Embedding Model Citation
 
 ```bibtex
@@ -146,8 +149,21 @@ If you use this dataset, please cite:
 }
 ```
 
+```bibtex
+@misc{clipvitb32,
+  title={CLIP ViT-B/32},
+  author={Open AI},
+  year={2021},
+  url={https://huggingface.co/sentence-transformers/clip-ViT-B-32}
+}
+```
+
 ### Acknowledgments
 
-- Original dataset: mixedbread-ai/wikipedia-data-en-2023-11
-- Embedding model: google/embeddinggemma-300m
+- Original dataset: 
+  - mixedbread-ai/wikipedia-data-en-2023-11
+  - LAION-400M
+- Embedding model: 
+  - google/embeddinggemma-300m
+  - sentence-transformers/clip-ViT-B-32
 - Benchmark framework: VectorDBBench